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Presentation: Patterns of Streaming Applications

Track: Emerging Trends in Data Engineering

Location: Ballroom BC

Duration: 2:55pm - 3:45pm

Day of week: Tuesday

Level: Intermediate

Persona: Architect, Backend Developer, Data Engineering

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Abstract

Stream processing engines are becoming pivotal in analyzing data. They have evolved beyond a data transport and simple processing machinery, to one that's capable of complex processing. The necessary features and building blocks of these engines are well known. And most capable engines have a familiar Dataflow based programming model.

As with any new paradigm, building streaming applications requires a different mindset and approach. Hence there is a need for identifying and describing patterns and anti-patterns for building these applications. Currently this mindshare is scarce.

Drawn from my experience working with several engineers within and outside of Netflix, this talk will present the following:

  • A blueprint for streaming data architectures and a review of desirable features of a streaming engine
  • Streaming Application patterns and anti-patterns
  • Use cases and concrete examples using Flink

Attendees will come away with patterns that can be applied to any capable stream processing framework such as Apache Flink.

Speaker: Monal Daxini

Distributed Systems Engineer / Leader @Netflix

Monal Daxini is the Tech Lead for Stream Processing platform for business insights at Netflix. He helped build the petabyte scale Keystone pipeline running on the Flink powered platform. He introduced Flink to Netflix, and also helped define the vision for this platform. He has over 17 years of experience building scalable distributed systems. He has a Masters in Computer Science, and has shared his thoughts and learnings at several conferences.

Find Monal Daxini at

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